{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vision-based-multi-task-manipulation-for","title":"Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration","arxiv_id":"1707.02920","date":"2017-07-10","proceeding":null,"authors":["Rouhollah Rahmatizadeh","Pooya Abolghasemi","Ladislau Bölöni","Sergey Levine"],"abstract":"We propose a technique for multi-task learning from demonstration that trains\nthe controller of a low-cost robotic arm to accomplish several complex picking\nand placing tasks, as well as non-prehensile manipulation. The controller is a\nrecurrent neural network using raw images as input and generating robot arm\ntrajectories, with the parameters shared across the tasks. The controller also\ncombines VAE-GAN-based reconstruction with autoregressive multimodal action\nprediction. Our results demonstrate that it is possible to learn complex\nmanipulation tasks, such as picking up a towel, wiping an object, and\ndepositing the towel to its previous position, entirely from raw images with\ndirect behavior cloning. We show that weight sharing and reconstruction-based\nregularization substantially improve generalization and robustness, and\ntraining on multiple tasks simultaneously increases the success rate on all\ntasks.","url_abs":"http://arxiv.org/abs/1707.02920v2","url_pdf":"http://arxiv.org/pdf/1707.02920v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vision-based-multi-task-manipulation-for","repo_url":"https://github.com/rrahmati/roboinstruct-2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.02920","atlas_url":"https://app.syntology.ai/?focus=1707.02920","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}